Tail events are rare or otherwise difficult to predict events that may produce profound outcomes affecting operations of an organization. Past tail events could have been predicted through in-depth analysis of historical data trends and other indicators of the likelihood of the tail event. Unfortunately, it is difficult to predict with certainty what type of tail event will occur, and when such a tail event would occur. It is possible, however, to prepare for multiple tail events, each at different potential future time points, by predetermining appropriate steps to address an occurrence of a tail event.
Therefore, a need exists for an automated network of systems, devices, and data feeds that is capable of efficiently predicting potential tail events, predicting potential tail event outcomes, determining appropriate action steps for responding to each predicted tail event, and automatically implementing such action steps when the associated tail event does occur.